Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is ba...Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding （NNSC） and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis （PCA） and non-negative matrix factorization （NMF） via simulations.展开更多
随着机器人技术的发展,仿人机器人在多个领域展现出应用潜力和价值。针对仿人机器人,基于机器视觉进行自主抓取研究,旨在提高仿人机器人在自然环境中的抓取适应能力和动作仿人性。在机器视觉方面,硬件采用Realsense-D435深度摄像头,利用...随着机器人技术的发展,仿人机器人在多个领域展现出应用潜力和价值。针对仿人机器人,基于机器视觉进行自主抓取研究,旨在提高仿人机器人在自然环境中的抓取适应能力和动作仿人性。在机器视觉方面,硬件采用Realsense-D435深度摄像头,利用YOLO(You Only Look Once)物体检测模型实现目标物体的识别、空间定位、深度图裁切和目标点云生成,并根据目标点云与标准点云的配准算法(ICP)获取物体的姿态,通过D-H法对机器人头部进行建模,将物体的位置和姿态由相机坐标系转换为机器人坐标系。在运动规划上,参照人手臂的抓取规律,将抓取过程分为9个基础动作:初始位、移动至预抓取位、抓取物体、提起物体、搬运物体、移动至放置位、放置物体、退出位和回到初始位,针对不同物体确定对应抓取姿态,以提高抓取成功率,根据视觉获取的抓取点和放置点,自主计算余下的关键点,并以空间弧形作为抓取轨迹,通过Matlab仿真,验证抓取过程机械臂末端运动轨迹和关节轨迹的合理性。最后进行物体抓取实验,结果表明,仿人机器人在自然环境中能够快速准确地识别和定位不同物体,并能成功进行抓取和搬运,成功率均在80%以上,并且能够兼顾动作的仿人性,验证了所提出方案的有效性。本研究可促进仿人机器人在人类日常生活中的应用和普及。展开更多
基金supported by the Prominent Youth Fund of the National Natural Science Foundation of China (61025006)
文摘Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding （NNSC） and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis （PCA） and non-negative matrix factorization （NMF） via simulations.
文摘随着机器人技术的发展,仿人机器人在多个领域展现出应用潜力和价值。针对仿人机器人,基于机器视觉进行自主抓取研究,旨在提高仿人机器人在自然环境中的抓取适应能力和动作仿人性。在机器视觉方面,硬件采用Realsense-D435深度摄像头,利用YOLO(You Only Look Once)物体检测模型实现目标物体的识别、空间定位、深度图裁切和目标点云生成,并根据目标点云与标准点云的配准算法(ICP)获取物体的姿态,通过D-H法对机器人头部进行建模,将物体的位置和姿态由相机坐标系转换为机器人坐标系。在运动规划上,参照人手臂的抓取规律,将抓取过程分为9个基础动作:初始位、移动至预抓取位、抓取物体、提起物体、搬运物体、移动至放置位、放置物体、退出位和回到初始位,针对不同物体确定对应抓取姿态,以提高抓取成功率,根据视觉获取的抓取点和放置点,自主计算余下的关键点,并以空间弧形作为抓取轨迹,通过Matlab仿真,验证抓取过程机械臂末端运动轨迹和关节轨迹的合理性。最后进行物体抓取实验,结果表明,仿人机器人在自然环境中能够快速准确地识别和定位不同物体,并能成功进行抓取和搬运,成功率均在80%以上,并且能够兼顾动作的仿人性,验证了所提出方案的有效性。本研究可促进仿人机器人在人类日常生活中的应用和普及。
